Running R Studio and Jupyter notebook using docker

“I am going to assume you have probably heard about docker, this post is if you want to just get your R Studio and Jupyter Notebook”

R Studio

Getting R Studio running in a container is relatively straight forward.

docker run -d -p 8787:8787 -e PASSWORD=yourpassword -v /"$PWD":/home/rstudio/name_of_folder rocker/rstudio

Let’s breakdown what each step is doing,

docker run - does and docker create and docker start at the same time. If the image does not exist it will pull.

-d - will run the docker container in the background. Allows you to close your terminal.

-p - assigns the port to connect to your container localhost:container

-e - Allows us to pass environment variables to the container. In this case we are passing the password.

-v - Mounting a volume to persist the results of our analysis. (remember containers are ephemeral). In this case full_path_locally:path_in_container. If the path in container does not exist it will be created automatically.

When you run this code you can visit your browser and type localhost:8787. Enter user as rstudio and your password.

Jupyter Notebook

Since you’ve got R Studio running already I will make this one short. The benefit with the Jupyer Notebook is that you can run both python 3 and R kernels. Makes them an amazing choice for writing a markdown document.

docker run -d -p 8888:8888 -e JUPYTER_TOKEN="yourpassword" -v "$PWD":/home/jovyan/work jupyter/datascience-notebook

When you run this code you can visit your browser and type localhost:8888. Enter token as your password.

To check all installed packages. Start a new python 3 kernel in the work folder and run:

import pkg_resources 
dists = [d for d in pkg_resources.working_set]
dist

A better way : Docker Compose

I know what you’re thinking. “There must be a better way to run both containers at the same time.” You’re right. There is. It’s docker compose.

Docker compose uses a YAML configuration file to spin up multiple containers at the same time.

There’s just a bit more work to it but it’s all worth it.

Your working directory should look like this.

data folder containing the data you plan on reading in. This will also be the folder used to mount to the container.
config folder containing your .env files to pass variables to the container. Like the token and password.
services folder - inside you should have a folder for each service in our case rstudio and jupyter in each of the folders you can put in a Dockerfile. (We’ll talk about that some other time)
docker-compose.yml - This is the main file that will be run. The configuration will glue all the parts together.

Lets get started

Pip install Docker Compose

pip install docker-compose

Clone the Repo

git clone https://github.com/zanalytics/docker-rstudio-jupyter.git
cd docker-rstudio-jupyter 

Spin up both containers
```
docker-compose up -d --build 
```

Stoping all containers

 docker stop $(docker ps -a -q)
 docker rm $(docker ps -a -q)

Deleting all images
```
 docker rmi $(docker images -a -q)
```

Hope you enjoyed see you next time.